Long Time Scale Molecular Dynamics Simulation of Magnesium Hydride Dehydrogenation Enabled by Machine Learning Interatomic Potentials

Magnesium hydride (MgH₂) is a promising material for solid-state hydrogen storage due to its high gravimetric hydrogen capacity as well as the abundance and low cost of magnesium. The material's limiting factor is the high dehydrogenation temperature (over 300 °C) and sluggish (de)hydrogenation kinetics when no catalyst is present, making it impractical for onboard applications. Catalysts and physical restructuring (e.g., through ball milling) have both shown kinetic improvements, without full theoretical understanding as to why. In this work, we developed a machine learning interatomic potential (MLP) for the Mg-H system, which was used to run long time scale molecular dynamics (MD) simulations of a thick magnesium hydride surface slab for up to 1 ns. Our MLP-based MD simulations reveal previously unreported behavior of subsurface molecular H₂ formation and subsequent trapping in the subsurface layer of MgH₂. This hindered diffusion of subsurface H₂ offers a partial explanation on the slow dehydrogenation kinetics of MgH₂. The kinetics will be improved if a catalyst obstructs subsurface formation and trapping of H₂ or if the diffusion of subsurface H₂ is improved through defects created by physical restructuring.